Overview

Dataset statistics

Number of variables31
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory242.3 KiB
Average record size in memory248.1 B

Variable types

Numeric13
Categorical17
Boolean1

Alerts

months_as_customer is highly overall correlated with ageHigh correlation
age is highly overall correlated with months_as_customerHigh correlation
total_claim_amount is highly overall correlated with incident_type and 6 other fieldsHigh correlation
injury_claim is highly overall correlated with incident_type and 6 other fieldsHigh correlation
property_claim is highly overall correlated with incident_type and 6 other fieldsHigh correlation
vehicle_claim is highly overall correlated with incident_type and 6 other fieldsHigh correlation
incident_type is highly overall correlated with collision_type and 7 other fieldsHigh correlation
collision_type is highly overall correlated with incident_type and 7 other fieldsHigh correlation
incident_severity is highly overall correlated with incident_type and 6 other fieldsHigh correlation
number_of_vehicles_involved is highly overall correlated with incident_type and 1 other fieldsHigh correlation
fraud_reported is highly overall correlated with incident_severityHigh correlation
authorities_contacted is highly overall correlated with incident_type and 5 other fieldsHigh correlation
umbrella_limit has 798 (79.8%) zerosZeros
capital-gains has 508 (50.8%) zerosZeros
capital-loss has 475 (47.5%) zerosZeros
incident_hour_of_the_day has 52 (5.2%) zerosZeros
injury_claim has 25 (2.5%) zerosZeros
property_claim has 19 (1.9%) zerosZeros

Reproduction

Analysis started2023-10-31 07:57:47.956998
Analysis finished2023-10-31 07:58:25.402867
Duration37.45 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

months_as_customer
Real number (ℝ)

Distinct391
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.954
Minimum0
Maximum479
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:25.498253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.9
Q1115.75
median199.5
Q3276.25
95-th percentile429.05
Maximum479
Range479
Interquartile range (IQR)160.5

Descriptive statistics

Standard deviation115.11317
Coefficient of variation (CV)0.56440754
Kurtosis-0.48542807
Mean203.954
Median Absolute Deviation (MAD)80.5
Skewness0.36217685
Sum203954
Variance13251.043
MonotonicityNot monotonic
2023-10-31T07:58:25.763286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194 8
 
0.8%
128 7
 
0.7%
254 7
 
0.7%
140 7
 
0.7%
210 7
 
0.7%
230 7
 
0.7%
285 7
 
0.7%
101 7
 
0.7%
239 6
 
0.6%
126 6
 
0.6%
Other values (381) 931
93.1%
ValueCountFrequency (%)
0 1
 
0.1%
1 3
0.3%
2 2
0.2%
3 2
0.2%
4 3
0.3%
5 2
0.2%
6 1
 
0.1%
7 1
 
0.1%
8 3
0.3%
9 2
0.2%
ValueCountFrequency (%)
479 2
0.2%
478 2
0.2%
476 1
0.1%
475 2
0.2%
473 1
0.1%
472 1
0.1%
468 1
0.1%
467 1
0.1%
465 1
0.1%
464 1
0.1%

age
Real number (ℝ)

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.948
Minimum19
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:25.901780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile26
Q132
median38
Q344
95-th percentile57
Maximum64
Range45
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.1402867
Coefficient of variation (CV)0.23467923
Kurtosis-0.26025502
Mean38.948
Median Absolute Deviation (MAD)6
Skewness0.47898805
Sum38948
Variance83.544841
MonotonicityNot monotonic
2023-10-31T07:58:26.033990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
43 49
 
4.9%
39 48
 
4.8%
41 45
 
4.5%
34 44
 
4.4%
38 42
 
4.2%
30 42
 
4.2%
31 42
 
4.2%
37 41
 
4.1%
33 39
 
3.9%
40 38
 
3.8%
Other values (36) 570
57.0%
ValueCountFrequency (%)
19 1
 
0.1%
20 1
 
0.1%
21 6
 
0.6%
22 1
 
0.1%
23 7
 
0.7%
24 10
 
1.0%
25 14
1.4%
26 26
2.6%
27 24
2.4%
28 30
3.0%
ValueCountFrequency (%)
64 2
 
0.2%
63 2
 
0.2%
62 4
 
0.4%
61 10
1.0%
60 9
0.9%
59 5
 
0.5%
58 8
0.8%
57 16
1.6%
56 8
0.8%
55 14
1.4%

policy_state
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
OH
352 
IL
338 
IN
310 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowIN
3rd rowOH
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
OH 352
35.2%
IL 338
33.8%
IN 310
31.0%

Length

2023-10-31T07:58:26.284309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:26.421958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
oh 352
35.2%
il 338
33.8%
in 310
31.0%

Most occurring characters

ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1000
351 
500
342 
2000
307 

Length

Max length4
Median length4
Mean length3.658
Min length3

Characters and Unicode

Total characters3658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000
2nd row2000
3rd row2000
4th row2000
5th row1000

Common Values

ValueCountFrequency (%)
1000 351
35.1%
500 342
34.2%
2000 307
30.7%

Length

2023-10-31T07:58:26.520894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:26.613289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1000 351
35.1%
500 342
34.2%
2000 307
30.7%

Most occurring characters

ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

policy_annual_premium
Real number (ℝ)

Distinct991
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1256.4061
Minimum433.33
Maximum2047.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:26.765802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum433.33
5-th percentile855.112
Q11089.6075
median1257.2
Q31415.695
95-th percentile1653.4435
Maximum2047.59
Range1614.26
Interquartile range (IQR)326.0875

Descriptive statistics

Standard deviation244.16739
Coefficient of variation (CV)0.19433795
Kurtosis0.07388944
Mean1256.4061
Median Absolute Deviation (MAD)164.26
Skewness0.0044019945
Sum1256406.1
Variance59617.717
MonotonicityNot monotonic
2023-10-31T07:58:26.911353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1558.29 2
 
0.2%
1215.36 2
 
0.2%
1362.87 2
 
0.2%
1073.83 2
 
0.2%
1389.13 2
 
0.2%
1074.07 2
 
0.2%
1374.22 2
 
0.2%
1524.45 2
 
0.2%
1281.25 2
 
0.2%
1230.69 1
 
0.1%
Other values (981) 981
98.1%
ValueCountFrequency (%)
433.33 1
0.1%
484.67 1
0.1%
538.17 1
0.1%
566.11 1
0.1%
617.11 1
0.1%
625.08 1
0.1%
653.66 1
0.1%
664.86 1
0.1%
671.01 1
0.1%
671.92 1
0.1%
ValueCountFrequency (%)
2047.59 1
0.1%
1969.63 1
0.1%
1935.85 1
0.1%
1927.87 1
0.1%
1922.84 1
0.1%
1896.91 1
0.1%
1878.44 1
0.1%
1865.83 1
0.1%
1863.04 1
0.1%
1861.43 1
0.1%

umbrella_limit
Real number (ℝ)

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1101000
Minimum-1000000
Maximum10000000
Zeros798
Zeros (%)79.8%
Negative1
Negative (%)0.1%
Memory size7.9 KiB
2023-10-31T07:58:27.042740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1000000
5-th percentile0
Q10
median0
Q30
95-th percentile6000000
Maximum10000000
Range11000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2297406.6
Coefficient of variation (CV)2.0866545
Kurtosis1.7920773
Mean1101000
Median Absolute Deviation (MAD)0
Skewness1.8067122
Sum1.101 × 109
Variance5.2780771 × 1012
MonotonicityNot monotonic
2023-10-31T07:58:27.152138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 798
79.8%
6000000 57
 
5.7%
5000000 46
 
4.6%
4000000 39
 
3.9%
7000000 29
 
2.9%
3000000 12
 
1.2%
8000000 8
 
0.8%
9000000 5
 
0.5%
2000000 3
 
0.3%
10000000 2
 
0.2%
ValueCountFrequency (%)
-1000000 1
 
0.1%
0 798
79.8%
2000000 3
 
0.3%
3000000 12
 
1.2%
4000000 39
 
3.9%
5000000 46
 
4.6%
6000000 57
 
5.7%
7000000 29
 
2.9%
8000000 8
 
0.8%
9000000 5
 
0.5%
ValueCountFrequency (%)
10000000 2
 
0.2%
9000000 5
 
0.5%
8000000 8
 
0.8%
7000000 29
 
2.9%
6000000 57
 
5.7%
5000000 46
 
4.6%
4000000 39
 
3.9%
3000000 12
 
1.2%
2000000 3
 
0.3%
0 798
79.8%

insured_zip
Real number (ℝ)

Distinct995
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501214.49
Minimum430104
Maximum620962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:27.282110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum430104
5-th percentile433273.75
Q1448404.5
median466445.5
Q3603251
95-th percentile617463.35
Maximum620962
Range190858
Interquartile range (IQR)154846.5

Descriptive statistics

Standard deviation71701.611
Coefficient of variation (CV)0.14305574
Kurtosis-1.1907111
Mean501214.49
Median Absolute Deviation (MAD)21841
Skewness0.81655393
Sum5.0121449 × 108
Variance5.141121 × 109
MonotonicityNot monotonic
2023-10-31T07:58:27.408385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
477695 2
 
0.2%
469429 2
 
0.2%
446895 2
 
0.2%
431202 2
 
0.2%
456602 2
 
0.2%
466132 1
 
0.1%
452218 1
 
0.1%
608982 1
 
0.1%
459630 1
 
0.1%
453193 1
 
0.1%
Other values (985) 985
98.5%
ValueCountFrequency (%)
430104 1
0.1%
430141 1
0.1%
430232 1
0.1%
430380 1
0.1%
430567 1
0.1%
430621 1
0.1%
430632 1
0.1%
430665 1
0.1%
430714 1
0.1%
430832 1
0.1%
ValueCountFrequency (%)
620962 1
0.1%
620869 1
0.1%
620819 1
0.1%
620757 1
0.1%
620737 1
0.1%
620507 1
0.1%
620493 1
0.1%
620473 1
0.1%
620358 1
0.1%
620207 1
0.1%

insured_sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
FEMALE
537 
MALE
463 

Length

Max length6
Median length6
Mean length5.074
Min length4

Characters and Unicode

Total characters5074
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
FEMALE 537
53.7%
MALE 463
46.3%

Length

2023-10-31T07:58:27.569710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:27.657459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
female 537
53.7%
male 463
46.3%

Most occurring characters

ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5074
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%
Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
JD
161 
High School
160 
Associate
145 
MD
144 
Masters
143 
Other values (2)
247 

Length

Max length11
Median length9
Mean length5.905
Min length2

Characters and Unicode

Total characters5905
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMD
2nd rowMD
3rd rowPhD
4th rowPhD
5th rowAssociate

Common Values

ValueCountFrequency (%)
JD 161
16.1%
High School 160
16.0%
Associate 145
14.5%
MD 144
14.4%
Masters 143
14.3%
PhD 125
12.5%
College 122
12.2%

Length

2023-10-31T07:58:27.760221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:28.022599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
jd 161
13.9%
high 160
13.8%
school 160
13.8%
associate 145
12.5%
md 144
12.4%
masters 143
12.3%
phd 125
10.8%
college 122
10.5%

Most occurring characters

ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4155
70.4%
Uppercase Letter 1590
 
26.9%
Space Separator 160
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 587
14.1%
s 576
13.9%
e 532
12.8%
h 445
10.7%
l 404
9.7%
i 305
7.3%
c 305
7.3%
t 288
6.9%
a 288
6.9%
g 282
6.8%
Uppercase Letter
ValueCountFrequency (%)
D 430
27.0%
M 287
18.1%
J 161
 
10.1%
S 160
 
10.1%
H 160
 
10.1%
A 145
 
9.1%
P 125
 
7.9%
C 122
 
7.7%
Space Separator
ValueCountFrequency (%)
160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5745
97.3%
Common 160
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 587
 
10.2%
s 576
 
10.0%
e 532
 
9.3%
h 445
 
7.7%
D 430
 
7.5%
l 404
 
7.0%
i 305
 
5.3%
c 305
 
5.3%
t 288
 
5.0%
a 288
 
5.0%
Other values (9) 1585
27.6%
Common
ValueCountFrequency (%)
160
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%
Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
machine-op-inspct
93 
prof-specialty
85 
tech-support
78 
sales
76 
exec-managerial
76 
Other values (9)
592 

Length

Max length17
Median length16
Mean length13.521
Min length5

Characters and Unicode

Total characters13521
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcraft-repair
2nd rowmachine-op-inspct
3rd rowsales
4th rowarmed-forces
5th rowsales

Common Values

ValueCountFrequency (%)
machine-op-inspct 93
 
9.3%
prof-specialty 85
 
8.5%
tech-support 78
 
7.8%
sales 76
 
7.6%
exec-managerial 76
 
7.6%
craft-repair 74
 
7.4%
transport-moving 72
 
7.2%
other-service 71
 
7.1%
priv-house-serv 71
 
7.1%
armed-forces 69
 
6.9%
Other values (4) 235
23.5%

Length

2023-10-31T07:58:28.150784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
machine-op-inspct 93
 
9.3%
prof-specialty 85
 
8.5%
tech-support 78
 
7.8%
sales 76
 
7.6%
exec-managerial 76
 
7.6%
craft-repair 74
 
7.4%
transport-moving 72
 
7.2%
other-service 71
 
7.1%
priv-house-serv 71
 
7.1%
armed-forces 69
 
6.9%
Other values (4) 235
23.5%

Most occurring characters

ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12433
92.0%
Dash Punctuation 1088
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1543
12.4%
r 1379
11.1%
a 1062
 
8.5%
s 986
 
7.9%
i 922
 
7.4%
c 886
 
7.1%
p 792
 
6.4%
t 749
 
6.0%
o 674
 
5.4%
n 620
 
5.0%
Other values (10) 2820
22.7%
Dash Punctuation
ValueCountFrequency (%)
- 1088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12433
92.0%
Common 1088
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1543
12.4%
r 1379
11.1%
a 1062
 
8.5%
s 986
 
7.9%
i 922
 
7.4%
c 886
 
7.1%
p 792
 
6.4%
t 749
 
6.0%
o 674
 
5.4%
n 620
 
5.0%
Other values (10) 2820
22.7%
Common
ValueCountFrequency (%)
- 1088
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%
Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
own-child
183 
other-relative
177 
not-in-family
174 
husband
170 
wife
155 

Length

Max length14
Median length13
Mean length9.466
Min length4

Characters and Unicode

Total characters9466
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhusband
2nd rowother-relative
3rd rowown-child
4th rowunmarried
5th rowunmarried

Common Values

ValueCountFrequency (%)
own-child 183
18.3%
other-relative 177
17.7%
not-in-family 174
17.4%
husband 170
17.0%
wife 155
15.5%
unmarried 141
14.1%

Length

2023-10-31T07:58:28.287437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:28.399161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
own-child 183
18.3%
other-relative 177
17.7%
not-in-family 174
17.4%
husband 170
17.0%
wife 155
15.5%
unmarried 141
14.1%

Most occurring characters

ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8758
92.5%
Dash Punctuation 708
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1004
11.5%
n 842
 
9.6%
e 827
 
9.4%
a 662
 
7.6%
r 636
 
7.3%
l 534
 
6.1%
o 534
 
6.1%
h 530
 
6.1%
t 528
 
6.0%
d 494
 
5.6%
Other values (9) 2167
24.7%
Dash Punctuation
ValueCountFrequency (%)
- 708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8758
92.5%
Common 708
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1004
11.5%
n 842
 
9.6%
e 827
 
9.4%
a 662
 
7.6%
r 636
 
7.3%
l 534
 
6.1%
o 534
 
6.1%
h 530
 
6.1%
t 528
 
6.0%
d 494
 
5.6%
Other values (9) 2167
24.7%
Common
ValueCountFrequency (%)
- 708
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

capital-gains
Real number (ℝ)

Distinct338
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25126.1
Minimum0
Maximum100500
Zeros508
Zeros (%)50.8%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:28.546268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q351025
95-th percentile70615
Maximum100500
Range100500
Interquartile range (IQR)51025

Descriptive statistics

Standard deviation27872.188
Coefficient of variation (CV)1.1092922
Kurtosis-1.2767035
Mean25126.1
Median Absolute Deviation (MAD)0
Skewness0.47885023
Sum25126100
Variance7.7685885 × 108
MonotonicityNot monotonic
2023-10-31T07:58:28.677102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 508
50.8%
46300 5
 
0.5%
51500 4
 
0.4%
68500 4
 
0.4%
55600 3
 
0.3%
49700 3
 
0.3%
51700 3
 
0.3%
56700 3
 
0.3%
47600 3
 
0.3%
44000 3
 
0.3%
Other values (328) 461
46.1%
ValueCountFrequency (%)
0 508
50.8%
800 1
 
0.1%
10000 1
 
0.1%
11000 1
 
0.1%
12100 1
 
0.1%
12800 1
 
0.1%
13100 1
 
0.1%
14100 1
 
0.1%
16100 1
 
0.1%
17300 1
 
0.1%
ValueCountFrequency (%)
100500 1
0.1%
98800 1
0.1%
94800 1
0.1%
91900 1
0.1%
90700 1
0.1%
88800 1
0.1%
88400 1
0.1%
87800 1
0.1%
84900 1
0.1%
83900 1
0.1%

capital-loss
Real number (ℝ)

Distinct354
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26793.7
Minimum-111100
Maximum0
Zeros475
Zeros (%)47.5%
Negative525
Negative (%)52.5%
Memory size7.9 KiB
2023-10-31T07:58:28.824186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-111100
5-th percentile-72305
Q1-51500
median-23250
Q30
95-th percentile0
Maximum0
Range111100
Interquartile range (IQR)51500

Descriptive statistics

Standard deviation28104.097
Coefficient of variation (CV)-1.0489069
Kurtosis-1.3138745
Mean-26793.7
Median Absolute Deviation (MAD)23250
Skewness-0.39147194
Sum-26793700
Variance7.8984025 × 108
MonotonicityNot monotonic
2023-10-31T07:58:28.967569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
47.5%
-31700 5
 
0.5%
-53700 5
 
0.5%
-50300 5
 
0.5%
-45300 4
 
0.4%
-51000 4
 
0.4%
-32800 4
 
0.4%
-53800 4
 
0.4%
-49200 4
 
0.4%
-31400 4
 
0.4%
Other values (344) 486
48.6%
ValueCountFrequency (%)
-111100 1
0.1%
-93600 1
0.1%
-91400 1
0.1%
-91200 1
0.1%
-90600 1
0.1%
-90200 1
0.1%
-90100 1
0.1%
-89400 1
0.1%
-88300 1
0.1%
-87300 1
0.1%
ValueCountFrequency (%)
0 475
47.5%
-5700 1
 
0.1%
-6300 1
 
0.1%
-8500 1
 
0.1%
-10600 1
 
0.1%
-12100 1
 
0.1%
-13200 1
 
0.1%
-13800 1
 
0.1%
-15600 1
 
0.1%
-15700 2
 
0.2%

incident_type
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Multi-vehicle Collision
419 
Single Vehicle Collision
403 
Vehicle Theft
94 
Parked Car
84 

Length

Max length24
Median length23
Mean length21.371
Min length10

Characters and Unicode

Total characters21371
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle Vehicle Collision
2nd rowVehicle Theft
3rd rowMulti-vehicle Collision
4th rowSingle Vehicle Collision
5th rowVehicle Theft

Common Values

ValueCountFrequency (%)
Multi-vehicle Collision 419
41.9%
Single Vehicle Collision 403
40.3%
Vehicle Theft 94
 
9.4%
Parked Car 84
 
8.4%

Length

2023-10-31T07:58:29.089110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:29.313783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
collision 822
34.2%
vehicle 497
20.7%
multi-vehicle 419
17.4%
single 403
16.8%
theft 94
 
3.9%
parked 84
 
3.5%
car 84
 
3.5%

Most occurring characters

ValueCountFrequency (%)
l 3382
15.8%
i 3382
15.8%
e 2413
11.3%
o 1644
 
7.7%
1403
 
6.6%
n 1225
 
5.7%
h 1010
 
4.7%
c 916
 
4.3%
C 906
 
4.2%
s 822
 
3.8%
Other values (15) 4268
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17146
80.2%
Uppercase Letter 2403
 
11.2%
Space Separator 1403
 
6.6%
Dash Punctuation 419
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 3382
19.7%
i 3382
19.7%
e 2413
14.1%
o 1644
9.6%
n 1225
 
7.1%
h 1010
 
5.9%
c 916
 
5.3%
s 822
 
4.8%
t 513
 
3.0%
u 419
 
2.4%
Other values (7) 1420
8.3%
Uppercase Letter
ValueCountFrequency (%)
C 906
37.7%
V 497
20.7%
M 419
17.4%
S 403
16.8%
T 94
 
3.9%
P 84
 
3.5%
Space Separator
ValueCountFrequency (%)
1403
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19549
91.5%
Common 1822
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 3382
17.3%
i 3382
17.3%
e 2413
12.3%
o 1644
8.4%
n 1225
 
6.3%
h 1010
 
5.2%
c 916
 
4.7%
C 906
 
4.6%
s 822
 
4.2%
t 513
 
2.6%
Other values (13) 3336
17.1%
Common
ValueCountFrequency (%)
1403
77.0%
- 419
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 3382
15.8%
i 3382
15.8%
e 2413
11.3%
o 1644
 
7.7%
1403
 
6.6%
n 1225
 
5.7%
h 1010
 
4.7%
c 916
 
4.3%
C 906
 
4.2%
s 822
 
3.8%
Other values (15) 4268
20.0%

collision_type
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Rear Collision
292 
Side Collision
276 
Front Collision
254 
Not Known
178 

Length

Max length15
Median length14
Mean length13.364
Min length9

Characters and Unicode

Total characters13364
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSide Collision
2nd rowNot Known
3rd rowRear Collision
4th rowFront Collision
5th rowNot Known

Common Values

ValueCountFrequency (%)
Rear Collision 292
29.2%
Side Collision 276
27.6%
Front Collision 254
25.4%
Not Known 178
17.8%

Length

2023-10-31T07:58:29.436904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:29.566339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
collision 822
41.1%
rear 292
 
14.6%
side 276
 
13.8%
front 254
 
12.7%
not 178
 
8.9%
known 178
 
8.9%

Most occurring characters

ValueCountFrequency (%)
o 2254
16.9%
i 1920
14.4%
l 1644
12.3%
n 1432
10.7%
1000
7.5%
s 822
 
6.2%
C 822
 
6.2%
e 568
 
4.3%
r 546
 
4.1%
t 432
 
3.2%
Other values (8) 1924
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10364
77.6%
Uppercase Letter 2000
 
15.0%
Space Separator 1000
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2254
21.7%
i 1920
18.5%
l 1644
15.9%
n 1432
13.8%
s 822
 
7.9%
e 568
 
5.5%
r 546
 
5.3%
t 432
 
4.2%
a 292
 
2.8%
d 276
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 822
41.1%
R 292
 
14.6%
S 276
 
13.8%
F 254
 
12.7%
N 178
 
8.9%
K 178
 
8.9%
Space Separator
ValueCountFrequency (%)
1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12364
92.5%
Common 1000
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2254
18.2%
i 1920
15.5%
l 1644
13.3%
n 1432
11.6%
s 822
 
6.6%
C 822
 
6.6%
e 568
 
4.6%
r 546
 
4.4%
t 432
 
3.5%
R 292
 
2.4%
Other values (7) 1632
13.2%
Common
ValueCountFrequency (%)
1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2254
16.9%
i 1920
14.4%
l 1644
12.3%
n 1432
10.7%
1000
7.5%
s 822
 
6.2%
C 822
 
6.2%
e 568
 
4.3%
r 546
 
4.1%
t 432
 
3.2%
Other values (8) 1924
14.4%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minor Damage
354 
Total Loss
280 
Major Damage
276 
Trivial Damage
90 

Length

Max length14
Median length12
Mean length11.62
Min length10

Characters and Unicode

Total characters11620
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor Damage
2nd rowMinor Damage
3rd rowMinor Damage
4th rowMajor Damage
5th rowMinor Damage

Common Values

ValueCountFrequency (%)
Minor Damage 354
35.4%
Total Loss 280
28.0%
Major Damage 276
27.6%
Trivial Damage 90
 
9.0%

Length

2023-10-31T07:58:29.700237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:29.829800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
damage 720
36.0%
minor 354
17.7%
total 280
 
14.0%
loss 280
 
14.0%
major 276
 
13.8%
trivial 90
 
4.5%

Most occurring characters

ValueCountFrequency (%)
a 2086
18.0%
o 1190
10.2%
1000
 
8.6%
g 720
 
6.2%
m 720
 
6.2%
e 720
 
6.2%
r 720
 
6.2%
D 720
 
6.2%
M 630
 
5.4%
s 560
 
4.8%
Other values (8) 2554
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8620
74.2%
Uppercase Letter 2000
 
17.2%
Space Separator 1000
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2086
24.2%
o 1190
13.8%
g 720
 
8.4%
m 720
 
8.4%
e 720
 
8.4%
r 720
 
8.4%
s 560
 
6.5%
i 534
 
6.2%
l 370
 
4.3%
n 354
 
4.1%
Other values (3) 646
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
D 720
36.0%
M 630
31.5%
T 370
18.5%
L 280
 
14.0%
Space Separator
ValueCountFrequency (%)
1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10620
91.4%
Common 1000
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2086
19.6%
o 1190
11.2%
g 720
 
6.8%
m 720
 
6.8%
e 720
 
6.8%
r 720
 
6.8%
D 720
 
6.8%
M 630
 
5.9%
s 560
 
5.3%
i 534
 
5.0%
Other values (7) 2020
19.0%
Common
ValueCountFrequency (%)
1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2086
18.0%
o 1190
10.2%
1000
 
8.6%
g 720
 
6.2%
m 720
 
6.2%
e 720
 
6.2%
r 720
 
6.2%
D 720
 
6.2%
M 630
 
5.4%
s 560
 
4.8%
Other values (8) 2554
22.0%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Police
292 
Fire
223 
Other
198 
Ambulance
196 
None
91 

Length

Max length9
Median length6
Mean length5.762
Min length4

Characters and Unicode

Total characters5762
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPolice
2nd rowPolice
3rd rowPolice
4th rowPolice
5th rowNone

Common Values

ValueCountFrequency (%)
Police 292
29.2%
Fire 223
22.3%
Other 198
19.8%
Ambulance 196
19.6%
None 91
 
9.1%

Length

2023-10-31T07:58:29.958506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:30.054298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
police 292
29.2%
fire 223
22.3%
other 198
19.8%
ambulance 196
19.6%
none 91
 
9.1%

Most occurring characters

ValueCountFrequency (%)
e 1000
17.4%
i 515
 
8.9%
l 488
 
8.5%
c 488
 
8.5%
r 421
 
7.3%
o 383
 
6.6%
P 292
 
5.1%
n 287
 
5.0%
F 223
 
3.9%
h 198
 
3.4%
Other values (8) 1467
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4762
82.6%
Uppercase Letter 1000
 
17.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1000
21.0%
i 515
10.8%
l 488
10.2%
c 488
10.2%
r 421
8.8%
o 383
 
8.0%
n 287
 
6.0%
h 198
 
4.2%
t 198
 
4.2%
m 196
 
4.1%
Other values (3) 588
12.3%
Uppercase Letter
ValueCountFrequency (%)
P 292
29.2%
F 223
22.3%
O 198
19.8%
A 196
19.6%
N 91
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5762
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1000
17.4%
i 515
 
8.9%
l 488
 
8.5%
c 488
 
8.5%
r 421
 
7.3%
o 383
 
6.6%
P 292
 
5.1%
n 287
 
5.0%
F 223
 
3.9%
h 198
 
3.4%
Other values (8) 1467
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1000
17.4%
i 515
 
8.9%
l 488
 
8.5%
c 488
 
8.5%
r 421
 
7.3%
o 383
 
6.6%
P 292
 
5.1%
n 287
 
5.0%
F 223
 
3.9%
h 198
 
3.4%
Other values (8) 1467
25.5%

incident_state
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
NY
262 
SC
248 
WV
217 
VA
110 
NC
110 
Other values (2)
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSC
2nd rowVA
3rd rowNY
4th rowOH
5th rowNY

Common Values

ValueCountFrequency (%)
NY 262
26.2%
SC 248
24.8%
WV 217
21.7%
VA 110
11.0%
NC 110
11.0%
PA 30
 
3.0%
OH 23
 
2.3%

Length

2023-10-31T07:58:30.175241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:30.285294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ny 262
26.2%
sc 248
24.8%
wv 217
21.7%
va 110
11.0%
nc 110
11.0%
pa 30
 
3.0%
oh 23
 
2.3%

Most occurring characters

ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

incident_hour_of_the_day
Real number (ℝ)

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.644
Minimum0
Maximum23
Zeros52
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:30.405126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median12
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.9513729
Coefficient of variation (CV)0.59699184
Kurtosis-1.1929402
Mean11.644
Median Absolute Deviation (MAD)6
Skewness-0.035584466
Sum11644
Variance48.321586
MonotonicityNot monotonic
2023-10-31T07:58:30.516390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17 54
 
5.4%
3 53
 
5.3%
0 52
 
5.2%
23 51
 
5.1%
16 49
 
4.9%
13 46
 
4.6%
10 46
 
4.6%
4 46
 
4.6%
6 44
 
4.4%
9 43
 
4.3%
Other values (14) 516
51.6%
ValueCountFrequency (%)
0 52
5.2%
1 29
2.9%
2 31
3.1%
3 53
5.3%
4 46
4.6%
5 33
3.3%
6 44
4.4%
7 40
4.0%
8 36
3.6%
9 43
4.3%
ValueCountFrequency (%)
23 51
5.1%
22 38
3.8%
21 42
4.2%
20 34
3.4%
19 40
4.0%
18 41
4.1%
17 54
5.4%
16 49
4.9%
15 39
3.9%
14 43
4.3%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
581 
3
358 
4
 
31
2
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Length

2023-10-31T07:58:30.616751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:30.714302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

property_damage
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Not Known
360 
NO
338 
YES
302 

Length

Max length9
Median length3
Mean length4.822
Min length2

Characters and Unicode

Total characters4822
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd rowNot Known
3rd rowNO
4th rowNot Known
5th rowNO

Common Values

ValueCountFrequency (%)
Not Known 360
36.0%
NO 338
33.8%
YES 302
30.2%

Length

2023-10-31T07:58:30.887476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:30.999838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
not 360
26.5%
known 360
26.5%
no 338
24.9%
yes 302
22.2%

Most occurring characters

ValueCountFrequency (%)
o 720
14.9%
n 720
14.9%
N 698
14.5%
t 360
7.5%
360
7.5%
K 360
7.5%
w 360
7.5%
O 338
7.0%
Y 302
6.3%
E 302
6.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2302
47.7%
Lowercase Letter 2160
44.8%
Space Separator 360
 
7.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 698
30.3%
K 360
15.6%
O 338
14.7%
Y 302
13.1%
E 302
13.1%
S 302
13.1%
Lowercase Letter
ValueCountFrequency (%)
o 720
33.3%
n 720
33.3%
t 360
16.7%
w 360
16.7%
Space Separator
ValueCountFrequency (%)
360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4462
92.5%
Common 360
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 720
16.1%
n 720
16.1%
N 698
15.6%
t 360
8.1%
K 360
8.1%
w 360
8.1%
O 338
7.6%
Y 302
6.8%
E 302
6.8%
S 302
6.8%
Common
ValueCountFrequency (%)
360
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 720
14.9%
n 720
14.9%
N 698
14.5%
t 360
7.5%
360
7.5%
K 360
7.5%
w 360
7.5%
O 338
7.0%
Y 302
6.3%
E 302
6.3%

bodily_injuries
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
340 
2
332 
1
328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Length

2023-10-31T07:58:31.088674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:31.197052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring characters

ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

witnesses
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
258 
2
250 
0
249 
3
243 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Length

2023-10-31T07:58:31.291608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:31.391133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring characters

ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Not Known
343 
NO
343 
YES
314 

Length

Max length9
Median length3
Mean length4.715
Min length2

Characters and Unicode

Total characters4715
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd rowNot Known
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
Not Known 343
34.3%
NO 343
34.3%
YES 314
31.4%

Length

2023-10-31T07:58:31.531658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T07:58:31.639881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
not 343
25.5%
known 343
25.5%
no 343
25.5%
yes 314
23.4%

Most occurring characters

ValueCountFrequency (%)
N 686
14.5%
o 686
14.5%
n 686
14.5%
t 343
7.3%
343
7.3%
K 343
7.3%
w 343
7.3%
O 343
7.3%
Y 314
6.7%
E 314
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2314
49.1%
Lowercase Letter 2058
43.6%
Space Separator 343
 
7.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 686
29.6%
K 343
14.8%
O 343
14.8%
Y 314
13.6%
E 314
13.6%
S 314
13.6%
Lowercase Letter
ValueCountFrequency (%)
o 686
33.3%
n 686
33.3%
t 343
16.7%
w 343
16.7%
Space Separator
ValueCountFrequency (%)
343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4372
92.7%
Common 343
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 686
15.7%
o 686
15.7%
n 686
15.7%
t 343
7.8%
K 343
7.8%
w 343
7.8%
O 343
7.8%
Y 314
7.2%
E 314
7.2%
S 314
7.2%
Common
ValueCountFrequency (%)
343
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 686
14.5%
o 686
14.5%
n 686
14.5%
t 343
7.3%
343
7.3%
K 343
7.3%
w 343
7.3%
O 343
7.3%
Y 314
6.7%
E 314
6.7%

total_claim_amount
Real number (ℝ)

Distinct763
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52761.94
Minimum100
Maximum114920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:31.767923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4320
Q141812.5
median58055
Q370592.5
95-th percentile88413
Maximum114920
Range114820
Interquartile range (IQR)28780

Descriptive statistics

Standard deviation26401.533
Coefficient of variation (CV)0.50038974
Kurtosis-0.45408143
Mean52761.94
Median Absolute Deviation (MAD)13855
Skewness-0.59458199
Sum52761940
Variance6.9704095 × 108
MonotonicityNot monotonic
2023-10-31T07:58:31.940063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59400 5
 
0.5%
2640 4
 
0.4%
70400 4
 
0.4%
4320 4
 
0.4%
44200 4
 
0.4%
75400 4
 
0.4%
60600 4
 
0.4%
3190 4
 
0.4%
58500 4
 
0.4%
70290 4
 
0.4%
Other values (753) 959
95.9%
ValueCountFrequency (%)
100 1
 
0.1%
1920 1
 
0.1%
2160 1
 
0.1%
2250 1
 
0.1%
2400 1
 
0.1%
2520 1
 
0.1%
2640 4
0.4%
2700 2
0.2%
2800 1
 
0.1%
2860 1
 
0.1%
ValueCountFrequency (%)
114920 1
0.1%
112320 1
0.1%
108480 1
0.1%
108030 1
0.1%
107900 1
0.1%
105820 1
0.1%
105040 1
0.1%
104610 1
0.1%
103560 1
0.1%
101860 1
0.1%

injury_claim
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct638
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7433.42
Minimum0
Maximum21450
Zeros25
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:32.099050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14295
median6775
Q311305
95-th percentile15662
Maximum21450
Range21450
Interquartile range (IQR)7010

Descriptive statistics

Standard deviation4880.9519
Coefficient of variation (CV)0.65662264
Kurtosis-0.76308706
Mean7433.42
Median Absolute Deviation (MAD)3705
Skewness0.26481088
Sum7433420
Variance23823691
MonotonicityNot monotonic
2023-10-31T07:58:32.224938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
2.5%
640 7
 
0.7%
480 7
 
0.7%
660 5
 
0.5%
580 5
 
0.5%
13520 5
 
0.5%
1180 5
 
0.5%
860 5
 
0.5%
6340 5
 
0.5%
780 5
 
0.5%
Other values (628) 926
92.6%
ValueCountFrequency (%)
0 25
2.5%
10 1
 
0.1%
220 1
 
0.1%
250 1
 
0.1%
280 2
 
0.2%
290 1
 
0.1%
300 3
 
0.3%
330 2
 
0.2%
350 1
 
0.1%
360 1
 
0.1%
ValueCountFrequency (%)
21450 1
0.1%
21330 1
0.1%
20700 1
0.1%
19020 1
0.1%
18520 1
0.1%
18220 1
0.1%
18180 1
0.1%
18080 1
0.1%
18000 1
0.1%
17880 1
0.1%

property_claim
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct626
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7399.57
Minimum0
Maximum23670
Zeros19
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:32.517259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14445
median6750
Q310885
95-th percentile15540
Maximum23670
Range23670
Interquartile range (IQR)6440

Descriptive statistics

Standard deviation4824.7262
Coefficient of variation (CV)0.65202791
Kurtosis-0.37638631
Mean7399.57
Median Absolute Deviation (MAD)3290
Skewness0.37816878
Sum7399570
Variance23277983
MonotonicityNot monotonic
2023-10-31T07:58:32.670873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
1.9%
860 6
 
0.6%
480 5
 
0.5%
660 5
 
0.5%
10000 5
 
0.5%
640 5
 
0.5%
650 5
 
0.5%
11080 5
 
0.5%
840 4
 
0.4%
5310 4
 
0.4%
Other values (616) 937
93.7%
ValueCountFrequency (%)
0 19
1.9%
20 1
 
0.1%
240 1
 
0.1%
250 1
 
0.1%
260 1
 
0.1%
280 3
 
0.3%
290 2
 
0.2%
300 3
 
0.3%
320 3
 
0.3%
330 1
 
0.1%
ValueCountFrequency (%)
23670 1
0.1%
21810 1
0.1%
21630 1
0.1%
21580 1
0.1%
21240 1
0.1%
20550 1
0.1%
20310 1
0.1%
20280 1
0.1%
19950 1
0.1%
19650 1
0.1%

vehicle_claim
Real number (ℝ)

Distinct726
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37928.95
Minimum70
Maximum79560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:32.790063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile3273.5
Q130292.5
median42100
Q350822.5
95-th percentile63094.5
Maximum79560
Range79490
Interquartile range (IQR)20530

Descriptive statistics

Standard deviation18886.253
Coefficient of variation (CV)0.49793767
Kurtosis-0.44657292
Mean37928.95
Median Absolute Deviation (MAD)9840
Skewness-0.62109793
Sum37928950
Variance3.5669055 × 108
MonotonicityNot monotonic
2023-10-31T07:58:32.948279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5040 7
 
0.7%
3360 6
 
0.6%
52080 5
 
0.5%
4720 5
 
0.5%
3600 5
 
0.5%
44800 5
 
0.5%
33600 5
 
0.5%
42720 4
 
0.4%
41580 4
 
0.4%
35000 4
 
0.4%
Other values (716) 950
95.0%
ValueCountFrequency (%)
70 1
0.1%
1440 2
0.2%
1680 2
0.2%
1750 1
0.1%
1760 1
0.1%
1800 1
0.1%
1960 2
0.2%
1980 1
0.1%
2030 1
0.1%
2080 1
0.1%
ValueCountFrequency (%)
79560 1
0.1%
77760 1
0.1%
77670 2
0.2%
76400 1
0.1%
76000 1
0.1%
75600 1
0.1%
75530 1
0.1%
74790 1
0.1%
73620 1
0.1%
73260 1
0.1%

auto_make
Categorical

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Saab
80 
Dodge
80 
Suburu
80 
Nissan
78 
Chevrolet
76 
Other values (9)
606 

Length

Max length10
Median length9
Mean length5.703
Min length3

Characters and Unicode

Total characters5703
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaab
2nd rowMercedes
3rd rowDodge
4th rowChevrolet
5th rowAccura

Common Values

ValueCountFrequency (%)
Saab 80
 
8.0%
Dodge 80
 
8.0%
Suburu 80
 
8.0%
Nissan 78
 
7.8%
Chevrolet 76
 
7.6%
Ford 72
 
7.2%
BMW 72
 
7.2%
Toyota 70
 
7.0%
Audi 69
 
6.9%
Accura 68
 
6.8%
Other values (4) 255
25.5%

Length

2023-10-31T07:58:33.090973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
saab 80
 
8.0%
dodge 80
 
8.0%
suburu 80
 
8.0%
nissan 78
 
7.8%
chevrolet 76
 
7.6%
ford 72
 
7.2%
bmw 72
 
7.2%
toyota 70
 
7.0%
audi 69
 
6.9%
accura 68
 
6.8%
Other values (4) 255
25.5%

Most occurring characters

ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4559
79.9%
Uppercase Letter 1144
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 629
13.8%
a 499
10.9%
o 491
10.8%
u 377
 
8.3%
r 361
 
7.9%
d 341
 
7.5%
s 289
 
6.3%
c 201
 
4.4%
n 201
 
4.4%
b 160
 
3.5%
Other values (10) 1010
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 160
14.0%
M 137
12.0%
A 137
12.0%
D 80
7.0%
N 78
6.8%
C 76
 
6.6%
B 72
 
6.3%
F 72
 
6.3%
W 72
 
6.3%
T 70
 
6.1%
Other values (3) 190
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5703
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

auto_year
Real number (ℝ)

Distinct21
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.103
Minimum1995
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-10-31T07:58:33.251219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1995
Q12000
median2005
Q32010
95-th percentile2014
Maximum2015
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0158608
Coefficient of variation (CV)0.0030002752
Kurtosis-1.1718678
Mean2005.103
Median Absolute Deviation (MAD)5
Skewness-0.048288807
Sum2005103
Variance36.190582
MonotonicityNot monotonic
2023-10-31T07:58:33.416465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1995 56
 
5.6%
1999 55
 
5.5%
2005 54
 
5.4%
2006 53
 
5.3%
2011 53
 
5.3%
2007 52
 
5.2%
2003 51
 
5.1%
2009 50
 
5.0%
2010 50
 
5.0%
2013 49
 
4.9%
Other values (11) 477
47.7%
ValueCountFrequency (%)
1995 56
5.6%
1996 37
3.7%
1997 46
4.6%
1998 40
4.0%
1999 55
5.5%
2000 42
4.2%
2001 42
4.2%
2002 49
4.9%
2003 51
5.1%
2004 39
3.9%
ValueCountFrequency (%)
2015 47
4.7%
2014 44
4.4%
2013 49
4.9%
2012 46
4.6%
2011 53
5.3%
2010 50
5.0%
2009 50
5.0%
2008 45
4.5%
2007 52
5.2%
2006 53
5.3%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
753 
True
247 
ValueCountFrequency (%)
False 753
75.3%
True 247
 
24.7%
2023-10-31T07:58:33.535703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Interactions

2023-10-31T07:58:21.402599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:58.210919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:00.135349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:01.868776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:03.644556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:05.503739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:07.467319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:09.397903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:11.138334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:13.048797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:14.908121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:17.270148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:19.178079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:21.681557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:58.351058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:00.278039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:01.994587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:03.987728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:05.630736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:07.590868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:09.536898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:11.259881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:13.165529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:15.026497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:17.424485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:19.406709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:21.931195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:58.461980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:00.412125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:02.125251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:04.106366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:05.752273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:07.738528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:09.662460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:11.379813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:13.301851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:15.179656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:17.545662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:19.532196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:22.170552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:58.628468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:00.565769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:02.276583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:04.245131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:05.893663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:08.025020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:09.794235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:11.505284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:13.443649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:15.360168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:17.678356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:19.726141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:22.324140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:58.760108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:00.697094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:02.410422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:04.397099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:06.051847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:08.199415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:09.919538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:11.691450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:13.569485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:15.526133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:17.799134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:19.885980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:22.598684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:58.908438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:00.860758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:02.547679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:04.533428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:06.248390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:08.332876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:10.065746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:11.855870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:13.742594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:15.667550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:17.926398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:20.072461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:22.792012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:59.049731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:00.988273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:02.673598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:04.657586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:06.448101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:08.454736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:10.217149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:12.102269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:13.886405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:15.795338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:18.044988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:20.215792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:22.934539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:59.166143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:01.114065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:02.805866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:04.776501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:06.645024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:08.579540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:10.338249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:12.220479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:14.044353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:15.964429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:18.174313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:20.361239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:23.121453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:59.274765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:01.234872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:02.926183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:04.884232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:06.773705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:08.697405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:10.457550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:12.350591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:14.172256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:16.167142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:18.272062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:20.520128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:23.367635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:59.398807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:01.365595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:03.061225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:05.004778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:06.925004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:08.827848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:10.580458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:12.496931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:14.340988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:16.445768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:18.449072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:20.667218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:23.551576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:59.537229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:01.486445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:03.193807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:05.128929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:07.054404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:08.952494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:10.739780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:12.623482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:14.468534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:16.655782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:18.645183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:20.836486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:23.827489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:59.660119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:01.604421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:03.336951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:05.247929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:07.191386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:09.086532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:10.857662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:12.754307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:14.592586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:16.899196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:18.761057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:21.101062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:24.034461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:57:59.935973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:01.739927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:03.476982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:05.375786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:07.325622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:09.226473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:10.995340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:12.904377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:14.742027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:17.053410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:18.917637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-31T07:58:21.254724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-10-31T07:58:33.653167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-10-31T07:58:34.029448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-10-31T07:58:34.318935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-10-31T07:58:34.579080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-10-31T07:58:34.890686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-10-31T07:58:35.222372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-10-31T07:58:24.324816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-31T07:58:25.186139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

months_as_customeragepolicy_statepolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_relationshipcapital-gainscapital-lossincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_yearfraud_reported
032848OH10001406.910466132MALEMDcraft-repairhusband533000Single Vehicle CollisionSide CollisionMajor DamagePoliceSC51YES12YES7161065101302052080Saab2004Y
122842IN20001197.225000000468176MALEMDmachine-op-inspctother-relative00Vehicle TheftNot KnownMinor DamagePoliceVA81Not Known00Not Known50707807803510Mercedes2007Y
213429OH20001413.145000000430632FEMALEPhDsalesown-child351000Multi-vehicle CollisionRear CollisionMinor DamagePoliceNY73NO23NO346507700385023100Dodge2007N
325641IL20001415.746000000608117FEMALEPhDarmed-forcesunmarried48900-62400Single Vehicle CollisionFront CollisionMajor DamagePoliceOH51Not Known12NO634006340634050720Chevrolet2014Y
422844IL10001583.916000000610706MALEAssociatesalesunmarried66000-46000Vehicle TheftNot KnownMinor DamageNoneNY201NO01NO650013006504550Accura2009N
525639OH10001351.100478456FEMALEPhDtech-supportunmarried00Multi-vehicle CollisionRear CollisionMajor DamageFireSC193NO02NO641006410641051280Saab2003Y
613734IN10001333.350441716MALEPhDprof-specialtyhusband0-77000Multi-vehicle CollisionFront CollisionMinor DamagePoliceNY03Not Known00Not Known7865021450715050050Nissan2012N
716537IL10001137.030603195MALEAssociatetech-supportunmarried00Multi-vehicle CollisionFront CollisionTotal LossPoliceVA233Not Known22YES515909380938032830Audi2015N
82733IL5001442.990601734FEMALEPhDother-serviceown-child00Single Vehicle CollisionFront CollisionTotal LossPoliceWV211NO11YES277002770277022160Toyota2012N
921242IL5001315.680600983MALEPhDpriv-house-servwife0-39300Single Vehicle CollisionRear CollisionTotal LossOtherNC141NO21Not Known423004700470032900Saab1996N
months_as_customeragepolicy_statepolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_relationshipcapital-gainscapital-lossincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_yearfraud_reported
99028643IL5001564.433000000477644FEMALEMDprof-specialtyunmarried77500-32800Single Vehicle CollisionRear CollisionMinor DamageFireNY181Not Known22YES342903810381026670Jeep2013N
99125744OH10001280.880433981MALEMDother-serviceother-relative59400-32200Single Vehicle CollisionRear CollisionTotal LossOtherWV211NO01NO469800522041760Accura2002N
9929426IN500722.660433696MALEMDexec-managerialhusband503000Multi-vehicle CollisionFront CollisionMajor DamageFireOH63YES12YES367003670734025690Nissan2010N
99312428OH10001235.140443567MALEMDexec-managerialhusband0-32100Multi-vehicle CollisionSide CollisionTotal LossOtherOH203Not Known01Not Known602006020602048160Volkswagen2012N
99414130IN10001347.040430665MALEHigh Schoolsalesown-child0-82100Parked CarNot KnownMinor DamageNoneSC61Not Known12YES648054010804860Honda1996N
995338OH10001310.800431289FEMALEMasterscraft-repairunmarried00Single Vehicle CollisionFront CollisionMinor DamageFireNC201YES01Not Known8720017440872061040Honda2006N
99628541IL10001436.790608177FEMALEPhDprof-specialtywife709000Single Vehicle CollisionRear CollisionMajor DamageFireSC231YES23Not Known108480180801808072320Volkswagen2015N
99713034OH5001383.493000000442797FEMALEMastersarmed-forcesother-relative351000Multi-vehicle CollisionSide CollisionMinor DamagePoliceNC43Not Known23YES675007500750052500Suburu1996N
99845862IL20001356.925000000441714MALEAssociatehandlers-cleanerswife00Single Vehicle CollisionRear CollisionMajor DamageOtherNY21Not Known01YES469805220522036540Audi1998N
99945660OH1000766.190612260FEMALEAssociatesaleshusband00Parked CarNot KnownMinor DamagePoliceWV61Not Known03Not Known50604609203680Mercedes2007N